CN114660994B - Numerical control machine tool machining process decision optimization method, system and related equipment - Google Patents
Numerical control machine tool machining process decision optimization method, system and related equipment Download PDFInfo
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- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/4093—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by part programming, e.g. entry of geometrical information as taken from a technical drawing, combining this with machining and material information to obtain control information, named part programme, for the NC machine
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Abstract
The invention discloses a method, a system and related equipment for optimizing the decision of a machining process of a numerical control machine tool, wherein the method comprises the following steps: acquiring processing characteristic information corresponding to a part to be processed, wherein the processing characteristic information comprises a processing characteristic type and a processing area; acquiring a set of target tools, a set of target fixtures and a set of target machine tools according to the processing characteristic information; respectively setting corresponding influence weight values for a machine tool decision, a clamp decision, a cutting parameter decision and a tool advancing and retracting decision in the machining process; and establishing a process decision optimization model with the lowest energy consumption as a target according to the processing characteristic information, the set of target tools, the set of target fixtures, the set of target machine tools and the influence weight values, and performing optimization solution on the process decision optimization model through a preset genetic optimization algorithm to obtain a target decision. Compared with the prior art, the scheme of the invention is beneficial to improving the decision optimization efficiency.
Description
Technical Field
The invention relates to the technical field of numerical control machining, in particular to a method and a system for optimizing a machining process decision of a numerical control machine tool and related equipment.
Background
With the development of scientific technology, the application of numerical control machining technology is more and more extensive. In the process of numerical control machining, decisions on a tool, a machine tool, cutting parameters and the like are needed to achieve the purpose of reducing energy consumption, and then numerical control machining is performed on a workpiece according to the corresponding decisions.
In the prior art, a decision is usually made manually, that is, a user makes a decision according to experience to determine a corresponding tool, a machine tool, a cutting parameter, and the like. The problem in the prior art is that the manual decision making mode is not beneficial to improving the decision optimization efficiency, and is also not beneficial to improving the decision making effect and realizing better energy-saving effect.
Thus, there is still a need for improvement and development of the prior art.
Disclosure of Invention
The invention mainly aims to provide a decision optimization method, a decision optimization system and related equipment for a machining process of a numerical control machine tool, and aims to solve the problems that in the prior art, a scheme for manually making a decision on the machining process of the numerical control machine tool is not beneficial to improving decision optimization efficiency, is not beneficial to improving decision effect and realizes better energy-saving effect.
In order to achieve the above object, a first aspect of the present invention provides a method for optimizing a machining process decision of a numerical control machine, wherein the method for optimizing a machining process decision of a numerical control machine comprises:
acquiring processing characteristic information corresponding to a part to be processed, wherein the processing characteristic information comprises a processing characteristic type and a processing area;
acquiring a set of target tools, a set of target fixtures and a set of target machine tools according to the processing characteristic information;
respectively setting corresponding influence weight values for a machine tool decision, a clamp decision, a cutting parameter decision and a tool advancing and retracting decision in the machining process;
and establishing a process decision optimization model with the lowest energy consumption as a target according to the processing characteristic information, the set of target tools, the set of target fixtures, the set of target machine tools and the influence weight values, and performing optimization solution on the process decision optimization model through a preset genetic optimization algorithm to obtain a target decision.
Optionally, the obtaining of the processing feature information corresponding to the part to be processed includes:
acquiring a three-dimensional model of the part to be processed and a corresponding description sentence;
acquiring a target two-dimensional image set corresponding to the part to be processed according to the three-dimensional model of the part to be processed, wherein the target two-dimensional image set comprises a plurality of target two-dimensional images acquired by collecting the part to be processed from different visual angles;
and performing processing feature recognition on the part to be processed according to the description sentence, the target two-dimensional image and a pre-trained processing feature recognition model to obtain processing feature information corresponding to the part to be processed.
Optionally, the obtaining a target two-dimensional image set corresponding to the part to be processed according to the three-dimensional model of the part to be processed includes:
acquiring minimum bounding boxes of the three-dimensional model, and respectively taking each vertex of the minimum bounding box and each bounding surface center point as target viewpoints, wherein one bounding surface center point is the center point of one bounding surface of the minimum bounding box;
performing region division on each surrounding surface according to a preset region block size to obtain a plurality of surrounding surface sub-regions, obtaining model region complexity corresponding to each surrounding surface sub-region, and obtaining the number of viewpoints corresponding to each surrounding surface sub-region according to the model region complexity and a preset complexity range, wherein the model region complexity corresponding to one surrounding surface sub-region is used for representing the surface undulation change degree of the model on one side of the three-dimensional model corresponding to the surrounding surface sub-region;
uniformly adding target viewpoints in each surrounding surface sub-area according to the number of viewpoints corresponding to each surrounding surface sub-area;
and taking the connecting line direction of each target viewpoint and a target central point as the view angle direction of each target viewpoint, acquiring the target two-dimensional image according to each target viewpoint, and marking the view point and the view angle of each target two-dimensional image, wherein the target central point is the central point of the three-dimensional model or the central point of the minimum bounding box.
Optionally, the bounding surface sub-regions are obtained by performing random region division on the bounding surface, and the complexity of the model region corresponding to one bounding surface sub-region is calculated by the following steps:
acquiring measuring points in the surrounding surface sub-area according to the preset number of the measuring points, wherein the measuring points are uniformly distributed in the surrounding surface sub-area;
obtaining measurement line segments according to the measurement points, wherein the starting point of the measurement line segment is the measurement point, the end point of the measurement line segment is a point on the surface of the three-dimensional model, and the straight line of each measurement line segment is perpendicular to the surrounding surface sub-area;
and calculating the variance of the length values of all the measuring line segments corresponding to the surrounding surface sub-region and taking the variance as the complexity of the model region corresponding to the surrounding surface sub-region.
Optionally, the decision variables in the process decision optimization model include a target processing machine tool selected for each process, a target processing tool selected for each process, a target feed direction selected for each process, a processing order of each process, and a cutting parameter of each process.
Optionally, the preset genetic optimization algorithm is a third-generation non-dominated genetic algorithm.
Optionally, an objective function in the process decision optimization model is an energy consumption objective function, and the energy consumption objective function is obtained by performing weighted summation according to machine tool energy consumption of each procedure, clamping process energy consumption of each procedure, cutting processing energy consumption of each procedure, cutter use energy consumption of each procedure, and each corresponding influence weight value;
and in the optimization solving process, the reciprocal of the objective function is used as a fitness function, and the decision variables are optimized through the third-generation non-dominated genetic algorithm to obtain the objective decision.
The second aspect of the present invention provides a decision optimization system for a machining process of a numerical control machine, wherein the decision optimization system for a machining process of a numerical control machine comprises:
the processing characteristic information acquisition module is used for acquiring processing characteristic information corresponding to a part to be processed, wherein the processing characteristic information comprises a processing characteristic type and a processing area;
the target set acquisition module is used for acquiring a set of target tools, a set of target fixtures and a set of target machine tools according to the processing characteristic information;
the weight setting module is used for setting corresponding influence weight values for machine tool decision, clamp decision, cutting parameter decision and cutter advancing and retreating decision in the machining process respectively;
and the decision optimization module is used for establishing a process decision optimization model with the lowest energy consumption as a target according to the processing characteristic information, the set of the target tools, the set of the target fixtures, the set of the target machine tools and the influence weight values, and performing optimization solution on the process decision optimization model through a preset genetic optimization algorithm to obtain a target decision.
A third aspect of the present invention provides an intelligent terminal, where the intelligent terminal includes a memory, a processor, and a numerically controlled machine tool machining process decision optimization program stored in the memory and operable on the processor, and the numerically controlled machine tool machining process decision optimization program, when executed by the processor, implements the steps of any of the numerically controlled machine tool machining process decision optimization methods.
A fourth aspect of the present invention provides a computer-readable storage medium, where a numerically controlled machine tool machining process decision optimization program is stored on the computer-readable storage medium, and when executed by a processor, the computer-readable storage medium implements any of the steps of the above-mentioned numerically controlled machine tool machining process decision optimization method.
Therefore, in the scheme of the invention, the processing characteristic information corresponding to the part to be processed is obtained, wherein the processing characteristic information comprises the type of the processing characteristic and the processing area; acquiring a set of target tools, a set of target fixtures and a set of target machine tools according to the processing characteristic information; respectively setting corresponding influence weight values for a machine tool decision, a clamp decision, a cutting parameter decision and a tool advancing and retracting decision in the machining process; and establishing a process decision optimization model with the lowest energy consumption as a target according to the processing characteristic information, the set of target tools, the set of target fixtures, the set of target machine tools and the influence weight values, and performing optimization solution on the process decision optimization model through a preset genetic optimization algorithm to obtain a target decision. Compared with the scheme of manually making a decision on the machining process of the numerical control machine tool in the prior art, the scheme of the invention can obtain the machining characteristic information corresponding to the part to be machined without making a manual decision, so that the set of target tools, the set of target fixtures and the set of target machine tools are determined according to the machining characteristic information, the range of consideration in decision optimization is favorably reduced, and the decision optimization efficiency is improved. And a corresponding process decision optimization model (a corresponding mathematical model) is established, and then the process decision optimization model is optimized and solved through a preset genetic optimization algorithm to obtain a target decision. The method has the advantages that manual decision making is not needed, the decision making optimization of the numerical control machine tool machining process can be automatically carried out, the decision making optimization efficiency is favorably improved, the decision making effect is favorably improved, and the better energy-saving effect is realized.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the embodiments or the prior art description will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive labor.
Fig. 1 is a schematic flow chart of a method for optimizing a numerical control machine tool machining process decision according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a detailed process of step S200 in FIG. 1 according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a decision optimization system for a machining process of a numerically-controlled machine tool according to an embodiment of the present invention;
fig. 4 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when …" or "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings of the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and it will be appreciated by those skilled in the art that the present invention may be practiced without departing from the spirit and scope of the present invention, and therefore the present invention is not limited by the specific embodiments disclosed below.
With the development of scientific technology, the application of numerical control machining technology is more and more extensive. In the process of numerical control machining, decisions need to be made on a cutter, a machine tool, cutting parameters and the like so as to achieve the purpose of reducing energy consumption, and then the workpiece is subjected to numerical control machining according to the corresponding decisions.
In the prior art, a decision is usually made manually, that is, a user makes a decision according to experience to determine a corresponding tool, a machine tool, a cutting parameter, and the like. The problem in the prior art is that the manual decision making mode is not beneficial to improving the decision optimization efficiency, and is also not beneficial to improving the decision making effect and realizing a better energy saving effect.
Meanwhile, the traditional process decision optimization method is mainly carried out in a characteristic recognition stage, namely, decision optimization is carried out according to the overall shape of the part before the machining characteristic recognition is finished. Therefore, decision making is not facilitated according to the processing characteristics of specific processing requirements, and a globally optimal decision making scheme is also not facilitated.
In order to solve at least one of the problems, in the scheme of the invention, processing characteristic information corresponding to a part to be processed is acquired, wherein the processing characteristic information comprises a processing characteristic type and a processing area; acquiring a set of target tools, a set of target fixtures and a set of target machine tools according to the processing characteristic information; respectively setting corresponding influence weight values for a machine tool decision, a clamp decision, a cutting parameter decision and a tool advancing and retracting decision in the machining process; and establishing a process decision optimization model with the lowest energy consumption as a target according to the processing characteristic information, the set of target tools, the set of target fixtures, the set of target machine tools and the influence weight values, and performing optimization solution on the process decision optimization model through a preset genetic optimization algorithm to obtain a target decision.
Compared with the scheme of manually making a decision on the machining process of the numerical control machine tool in the prior art, the scheme of the invention can obtain the machining characteristic information corresponding to the part to be machined without making a manual decision, so that the set of target tools, the set of target fixtures and the set of target machine tools are determined according to the machining characteristic information, the range of consideration in decision optimization is favorably reduced, and the decision optimization efficiency is improved. And a corresponding process decision optimization model (a corresponding mathematical model) is established, and then the process decision optimization model is optimized and solved through a preset genetic optimization algorithm to obtain a target decision. The method has the advantages that manual decision making is not needed, the decision making optimization of the numerical control machine tool machining process can be automatically carried out, the decision making optimization efficiency is improved, the decision making effect is improved, and the better energy saving effect is realized.
Meanwhile, the specific decision optimization process of the numerical control machine tool machining process in the scheme of the invention is carried out after the machining characteristic identification is completed and the machining characteristic information is obtained, so that a globally optimal decision scheme is favorably obtained, and the decision effect is improved.
Exemplary method
As shown in fig. 1, an embodiment of the present invention provides a method for optimizing a numerical control machine tool machining process decision, specifically, the method includes the following steps:
step S100, obtaining machining characteristic information corresponding to a part to be machined, wherein the machining characteristic information comprises a machining characteristic type and a machining area.
In this embodiment, the decision optimization needs to be performed on the machining process of the numerical control machine corresponding to the part to be machined.
In this embodiment, as shown in fig. 2, the step S100 specifically includes the following steps:
and S101, acquiring a three-dimensional model of the part to be processed and a corresponding description sentence.
The three-dimensional model is a three-dimensional model corresponding to the part to be processed and can reflect the three-dimensional structure of the part to be processed, and the descriptive statement is a descriptive statement input by a target object (such as a user) and used for describing the processing characteristics of the part to be processed so as to identify the processing characteristics of the part to be processed by combining the descriptive statement and improve the efficiency of processing characteristic identification. In an application scenario, a description sentence describes an area with machining features in a part to be machined, and the machining features of the area are identified according to a preset machining feature model.
Step S102, a target two-dimensional image set corresponding to the part to be processed is obtained according to the three-dimensional model of the part to be processed, wherein the target two-dimensional image set comprises a plurality of target two-dimensional images acquired from different viewing angles of the part to be processed.
Specifically, the three-dimensional model of the part to be processed is collected in multiple angles, and a corresponding target two-dimensional image is obtained, so that processing feature recognition is realized.
The obtaining of the target two-dimensional image set corresponding to the part to be processed according to the three-dimensional model of the part to be processed includes:
acquiring minimum bounding boxes of the three-dimensional model, and respectively taking each vertex and each bounding surface central point of the minimum bounding box as target viewpoints, wherein one bounding surface central point is the central point of one bounding surface of the minimum bounding box;
performing area division on each enclosing surface according to a preset area block size to obtain a plurality of enclosing surface sub-areas, obtaining model area complexity corresponding to each enclosing surface sub-area, and obtaining the number of viewpoints corresponding to each enclosing surface sub-area according to the model area complexity and a preset complexity range, wherein the model area complexity corresponding to one enclosing surface sub-area is used for reflecting the fluctuation degree of the model surface of the three-dimensional model on one side corresponding to the enclosing surface sub-area;
uniformly adding target viewpoints in each surrounding surface sub-area according to the number of viewpoints corresponding to each surrounding surface sub-area;
and taking the connecting line direction of each target viewpoint and a target central point as the view angle direction of each target viewpoint, acquiring the target two-dimensional image according to each target viewpoint, and marking the view point and the view angle of each target two-dimensional image, wherein the target central point is the central point of the three-dimensional model or the central point of the minimum bounding box.
The minimum bounding box is a cuboid, a three-dimensional model of a part to be processed can be bounded inside the minimum bounding box, correspondingly, the minimum bounding box is provided with 8 vertexes and 6 bounding surfaces, and the center points of the vertexes and the bounding surfaces are respectively used as target viewpoints. And then, carrying out region division on each surrounding surface according to the preset region blocking size to obtain a plurality of surrounding surface sub-regions. The size of the region blocks is preset, and can be adjusted according to actual requirements, for example, the size of the region blocks can be set according to the complexity of a part to be processed, and the size of the region blocks set for the part to be processed with higher complexity is smaller, so that finer two-dimensional image acquisition is realized.
And according to the size of the region blocks, randomly dividing each surrounding surface to obtain a plurality of surrounding surface sub-regions, wherein the size of each surrounding surface sub-region is not larger than the size of the region blocks. And then obtaining the complexity of the model region corresponding to each bounding surface sub-region.
Specifically, the bounding surface sub-regions are obtained by performing random region division on the bounding surface, and the complexity of the model region corresponding to one bounding surface sub-region is calculated by the following steps:
acquiring measuring points in the surrounding surface sub-area according to the preset number of the measuring points, wherein the measuring points are uniformly distributed in the surrounding surface sub-area;
obtaining measurement line segments according to the measurement points, wherein the starting point of the measurement line segment is the measurement point, the end point of the measurement line segment is a point on the surface of the three-dimensional model, and the straight line where each measurement line segment is located is perpendicular to the surrounding surface sub-area;
and calculating the variance of the length values of all the measuring line segments corresponding to the surrounding surface sub-region and taking the variance as the complexity of the model region corresponding to the surrounding surface sub-region.
The number of the measuring points is the number of the measuring points in a preset surrounding surface subregion, and can be adjusted according to actual requirements, the number of the measuring points in the surrounding surface subregion is uniformly selected, then corresponding measuring line segments are obtained, and the fluctuation change of the model surface corresponding to the surrounding surface subregion is reflected by the difference between the length values of the measuring line segments, so that the complexity of the model region corresponding to the surrounding surface subregion can be determined according to the variance of the length values of the measuring line segments. Therefore, the more viewpoints are in a small area (surrounding surface sub-area) with more complex model fluctuation, and the acquired two-dimensional image of the target can better reflect the real structure of the three-dimensional model.
In an application scenario, after obtaining the model region complexity, the corresponding number of measurement points may be obtained directly based on the model region complexity, for example, the model region complexity is multiplied by a preset number reference value (e.g. 5) and then rounded to be used as the corresponding number of viewpoints.
In this embodiment, after obtaining the complexity of the model region, the number of viewpoints corresponding to each bounding surface may be obtained according to a preset complexity range, for example, for a first bounding surface sub-region, if the complexity of the model region thereof falls within a first range (for example, greater than or equal to 0 and less than 10), the number of viewpoints corresponding to the first bounding surface sub-region is set to a preset first number (for example, 5); for the second bounding surface sub-region, if the model region complexity thereof belongs to the second range (for example, greater than 10 and less than 20), the corresponding number of viewpoints is set to the preset second number (for example, 10), and the specific range division manner and the corresponding number value are not specifically limited and are merely illustrated as examples.
And step S103, performing machining feature recognition on the part to be machined according to the descriptive statement, the target two-dimensional image and a pre-trained machining feature recognition model to obtain machining feature information corresponding to the part to be machined.
In this embodiment, semantic recognition is performed on the description sentence to obtain a plurality of processing feature recognition keywords, and the processing feature recognition keywords are used as processing feature recognition information. And then, inputting the processing feature identification information and the target two-dimensional image into a pre-trained processing feature identification model to identify the processing feature of the part to be processed.
Specifically, the processing feature recognition model is a depth residual error network model trained in advance, and is trained in advance to recognize the processing features in the image according to the input processing feature recognition information and the image, and the depth residual error network model is trained in advance by the following steps:
acquiring a training data set, wherein the training data set comprises a plurality of training data, and the training data comprises training semantic information, training images and processing feature labeling information corresponding to the training images;
and performing iterative training on the depth residual error network model according to the training data set and a preset marking information error threshold value until a trained depth residual error network model is obtained, wherein the trained depth residual error network model performs processing feature recognition on input training semantic information and a training image, and the loss value between the obtained processing feature information and the processing feature marking information corresponding to the training image is not greater than the marking information error threshold value.
The training images are two-dimensional images, and all the training images in the training data may belong to the same part (that is, the same part is acquired by acquiring two-dimensional images at different angles), or may belong to different parts. When all training images in the training data belong to the same part, the training semantic information corresponding to each training image may be the same (when corresponding processing feature recognition is performed, each target two-dimensional image also shares the same processing feature recognition information), that is, all training images corresponding to the same part may have the same training semantic information, but are not limited specifically.
In an application scenario, each of the training data includes training semantic information, a plurality of training images and processing feature labeling information corresponding to the training images, and the training images may share one training semantic information.
The error threshold of the labeling information is a preset error threshold, the number of times of model updating can be preset, and when the precision of the depth residual error network model meets the requirement or reaches the number of times of model updating, the training is considered to be completed. The loss value between the processing feature information obtained by the corresponding recognition and the processing feature labeling information corresponding to the training image may be a difference value between the two, or may be a value obtained by calculation according to a preset loss function, which is not specifically limited herein.
It should be noted that, the processing characteristic information corresponding to the part to be processed in this embodiment may also be obtained by other manners, and is not limited specifically herein.
And step S200, acquiring a set of target tools, a set of target fixtures and a set of target machine tools according to the machining characteristic information.
Specifically, a set of target tools, a set of target fixtures and a set of target machine tools to be selected are obtained according to the type of machining features to be machined. The set of target tools includes a plurality of target tools, each target tool is a tool that needs to be used in the process flow for processing the to-be-processed part, and so on, each target fixture is a fixture that needs to be used in the process flow for processing the to-be-processed part, and each target machine tool is a machine tool that needs to be used in the process flow for processing the to-be-processed part.
In an application scenario, a corresponding relation table of machining characteristics, cutters, clamps and machine tools can be established in advance, and a target cutter, a target clamp and a target machine tool which are required to be selected for a part to be machined are determined according to the corresponding relation table.
And step S300, respectively setting corresponding influence weight values for machine tool decision, clamp decision, cutting parameter decision and tool advance and retreat decision in the machining process.
Specifically, in this embodiment, the objective of optimizing the machining process decision is to minimize the energy consumption required to be consumed, and the machine tool decision, the clamp decision, the cutting parameter decision and the tool feeding and retracting decision in the machining process of the numerical control machine tool all affect the energy consumption. In this embodiment, corresponding influence weight values may be set for a machine tool decision, a clamp decision, a cutting parameter decision, and a tool advance/retract decision, respectively, so as to adjust an optimization process, and perform better optimization on a part (a decision part with a larger influence weight value) that is more concerned by a user.
It should be noted that the influence weight values corresponding to the machine tool decision, the clamp decision, the cutting parameter decision, and the tool advance/retreat decision may be the same (for example, all of them are set to 1 or 0.25), or may be different, and in this embodiment, the same example (all of them are set to 1) is used for description, but the present invention is not limited specifically.
And step S400, establishing a process decision optimization model with the lowest energy consumption as a target according to the processing characteristic information, the set of target tools, the set of target fixtures, the set of target machine tools and the influence weight values, and performing optimization solution on the process decision optimization model through a preset genetic optimization algorithm to obtain a target decision.
Specifically, the decision variables in the process decision optimization model include a target machining tool selected for each process, a target cutting direction selected for each process, a machining order of each process, and a cutting parameter of each process. The predetermined genetic optimization algorithm is a third-generation non-dominated genetic algorithm (NSGA 3 algorithm).
In this embodiment, the objective function in the process decision optimization model is an energy consumption objective function, and the energy consumption objective function is obtained by performing weighted summation according to the machine tool energy consumption of each process, the clamping process energy consumption of each process, the cutting processing energy consumption of each process, the tool use energy consumption of each process, and each corresponding influence weight value;
and in the optimization solving process, the reciprocal of the objective function is used as a fitness function, and the decision variables are optimized through the third-generation non-dominated genetic algorithm to obtain the objective decision.
Specifically, the energy consumption of the machine tool is the energy consumption of the machine tool in the operation process, and comprises energy consumption in the idle cutting process, standby energy consumption and energy consumption in the cutting process, and the energy consumption can be obtained according to corresponding time and power. The energy consumption of the clamping process is the energy consumption of the clamping process of each procedure, the energy consumption of the cutting process is the energy consumption of the cutting process of each procedure, and the energy consumption of the cutter in use is the energy consumption of the cutter in use of each procedure. And weighting and summing the energy consumption to obtain an energy consumption target function, taking the reciprocal of the energy consumption target function as a fitness function of the NSGA3 algorithm, and performing decision optimization through the NSGA3 algorithm, wherein in the optimization process, the minimum energy consumption target function is taken as a target to obtain a corresponding group of target decisions. It should be noted that, in the process of performing decision optimization through the NSGA3 algorithm, a maximum optimization time may be preset as a condition for terminating optimization, so as to avoid a situation that optimization takes too long and an optimized structure cannot be obtained. And when the maximum optimization times is reached, taking a group of decisions which minimize the energy consumption objective function as the target decisions.
It should be noted that the selection of the processing machine tool and the selection of the tool in each process affect the selection range of the corresponding cutting parameter, so in an application scenario, corresponding constraint conditions are constructed for the process decision optimization model. The constraint conditions may include: the rotating speed of the cutter does not exceed the corresponding maximum rotating speed of the machine tool and is not lower than the corresponding minimum rotating speed of the machine tool; the feeding speed of the cutter is not more than the fastest feeding speed of the corresponding machine tool and is not lower than the lowest feeding speed of the corresponding machine tool; the power of the machine tool does not exceed the product of the effective power coefficient of the machine tool and the maximum power of the machine tool, wherein the effective power coefficient of the machine tool is a preset coefficient and is not more than 1; the cutter cutting force is not greater than the maximum cutting force of the machine tool. Other constraint conditions may also be set according to actual requirements, and are not specifically limited herein.
In one application scenario, during the decision optimization by the NSGA3 algorithm, a set of reference points (which may be randomly generated) is preset, and a set of initial population (composed of decision variables) containing N individuals is randomly generated, where N is the population size, and then the algorithm iterates until the termination condition is satisfied. In the process of any generation of iteration, generating child population (the size is N) by crossing and variation on the basis of the current population through an algorithm, combining the two child populations into a new population to be selected with the size of 2N, dividing the population to be selected to obtain different non-dominant layers, constructing a new target population (the size is N), and performing the next iteration by taking the target population as the current population after the iteration.
It should be noted that the decision optimization solution may also be performed based on other algorithms, for example, according to a simulated annealing algorithm, a particle swarm optimization, and the like, which is not specifically limited herein.
As can be seen from the above, in the method for optimizing the machining process decision of the numerical control machine tool provided by the embodiment of the present invention, the machining feature information corresponding to the part to be machined is obtained, where the machining feature information includes a machining feature type and a machining area; acquiring a set of target tools, a set of target fixtures and a set of target machine tools according to the processing characteristic information; respectively setting corresponding influence weight values for a machine tool decision, a clamp decision, a cutting parameter decision and a tool advancing and retracting decision in the machining process; and establishing a process decision optimization model with the lowest energy consumption as a target according to the processing characteristic information, the set of target tools, the set of target fixtures, the set of target machine tools and the influence weight values, and performing optimization solution on the process decision optimization model through a preset genetic optimization algorithm to obtain a target decision.
Compared with the scheme of manually making a decision on the machining process of the numerical control machine tool in the prior art, the scheme of the invention can obtain the machining characteristic information corresponding to the part to be machined without making a manual decision, so that the set of target tools, the set of target fixtures and the set of target machine tools are determined according to the machining characteristic information, the range of consideration in decision optimization is favorably reduced, and the decision optimization efficiency is improved. And a corresponding process decision optimization model (a corresponding mathematical model) is established, and then the process decision optimization model is optimized and solved through a preset genetic optimization algorithm to obtain a target decision. The method has the advantages that manual decision making is not needed, the optimization of the numerical control machine tool machining process decision making can be automatically carried out, the optimization efficiency is favorably improved, the decision is reasonably optimized, the decision making effect is favorably improved, and the better energy-saving effect is realized.
Exemplary device
As shown in fig. 3, an embodiment of the present invention further provides a decision optimization system for a machining process of a numerical control machine, corresponding to the decision optimization method for a machining process of a numerical control machine, where the decision optimization system for a machining process of a numerical control machine includes:
the processing feature information obtaining module 510 is configured to obtain processing feature information corresponding to a part to be processed, where the processing feature information includes a processing feature type and a processing area.
In this embodiment, the decision optimization needs to be performed on the machining process of the numerical control machine corresponding to the part to be machined.
And a target set acquiring module 520, configured to acquire a set of target tools, a set of target fixtures, and a set of target machine tools according to the machining feature information.
Specifically, a set of target tools, a set of target fixtures and a set of target machine tools to be selected are obtained according to the type of machining features to be machined. The set of target tools includes a plurality of target tools, each target tool is a tool that needs to be used in the process flow for processing the to-be-processed part, and so on, each target fixture is a fixture that needs to be used in the process flow for processing the to-be-processed part, and each target machine tool is a machine tool that needs to be used in the process flow for processing the to-be-processed part.
In an application scenario, a corresponding relation table of machining characteristics, tools, clamps and machine tools can be established in advance, and a target tool, a target clamp and a target machine tool which are required to be selected for a part to be machined are determined according to the corresponding relation table.
And the weight setting module 530 is configured to set corresponding influence weight values for a machine tool decision, a clamp decision, a cutting parameter decision and a tool advance and retreat decision in the machining process.
Specifically, in this embodiment, the objective of optimizing the machining process decision is to minimize the energy consumption required to be consumed, and the machine tool decision, the clamp decision, the cutting parameter decision and the tool feeding and retracting decision in the machining process of the numerical control machine tool all affect the energy consumption. In this embodiment, corresponding influence weight values may be set for a machine tool decision, a fixture decision, a cutting parameter decision, and a tool advance/retract decision, respectively, to adjust an optimization process, and perform better optimization on a part (decision part with a larger influence weight value) that is more concerned by a user.
It should be noted that the influence weight values corresponding to the machine tool decision, the clamp decision, the cutting parameter decision, and the tool advance/retract decision may be the same (for example, all set to 1 or 0.25) or may be different, and in this embodiment, the same example (all set to 1) is used for description, but the description is not limited to this.
And a decision optimization module 540, configured to establish a process decision optimization model with the lowest energy consumption as a target according to the processing feature information, the set of target tools, the set of target fixtures, the set of target machine tools, and each of the influence weight values, and perform optimization solution on the process decision optimization model through a preset genetic optimization algorithm to obtain a target decision.
Specifically, the decision variables in the process decision optimization model include a target machining tool selected for each process, a target cutting direction selected for each process, a machining order of each process, and a cutting parameter of each process. The predetermined genetic optimization algorithm is a third-generation non-dominated genetic algorithm (NSGA 3 algorithm).
Specifically, in this embodiment, the specific functions of the above-mentioned numerical control machine tool machining process decision optimization system and its modules may refer to the corresponding descriptions in the above-mentioned numerical control machine tool machining process decision optimization method, which are not described herein again.
It should be noted that, the dividing manner of each module of the above-mentioned numerically-controlled machine tool machining process decision optimization system is not unique, and is not specifically limited herein.
Based on the above embodiment, the present invention further provides an intelligent terminal, and a schematic block diagram thereof may be as shown in fig. 4. The intelligent terminal comprises a processor and a memory. The memory of the intelligent terminal comprises a numerical control machine tool machining process decision optimization program, and the memory provides an environment for the operation of the numerical control machine tool machining process decision optimization program. When being executed by a processor, the numerical control machine tool machining process decision optimization program realizes the steps of any one numerical control machine tool machining process decision optimization method. It should be noted that the above-mentioned intelligent terminal may further include other functional modules or units, which are not specifically limited herein.
It will be understood by those skilled in the art that the block diagram shown in fig. 4 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation to the intelligent terminal to which the solution of the present invention is applied, and in particular, the intelligent terminal may include more or less components than those shown in the figure, or combine some components, or have a different arrangement of components.
The embodiment of the invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is stored with a numerical control machine tool spindle error prediction and compensation program, and the numerical control machine tool spindle error prediction and compensation program is executed by a processor to realize the steps of any numerical control machine tool machining process decision optimization method provided by the embodiment of the invention.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the system may be divided into different functional units or modules to implement all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present invention. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art would appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed system/intelligent terminal and method can be implemented in other ways. For example, the above-described system/intelligent terminal embodiments are merely illustrative, and for example, the division of the above modules or units is only one logical function division, and the actual implementation may be implemented by another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The integrated modules/units described above, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments described above may be implemented. The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form. The computer readable medium may include: any entity or device capable of carrying the above-described computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier signal, telecommunications signal, software distribution medium, and the like. It should be noted that the contents contained in the computer-readable storage medium can be increased or decreased as required by legislation and patent practice in the jurisdiction.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (8)
1. The numerical control machine tool machining process decision optimization method is characterized by comprising the following steps of:
the method comprises the steps of obtaining machining feature information corresponding to a part to be machined, wherein the machining feature information comprises a machining feature type and a machining area;
acquiring a set of target tools, a set of target fixtures and a set of target machine tools according to the processing characteristic information;
respectively setting corresponding influence weight values for a machine tool decision, a clamp decision, a cutting parameter decision and a tool advancing and retracting decision in the machining process;
establishing a process decision optimization model with the lowest energy consumption as a target according to the processing characteristic information, the set of target tools, the set of target fixtures, the set of target machine tools and the influence weight values, and performing optimization solution on the process decision optimization model through a preset genetic optimization algorithm to obtain a target decision;
wherein, the processing characteristic information that the part that obtains to wait to process corresponds includes: acquiring a three-dimensional model of the part to be processed and a corresponding description sentence; acquiring a target two-dimensional image set corresponding to the part to be processed according to the three-dimensional model of the part to be processed, wherein the target two-dimensional image set comprises a plurality of target two-dimensional images acquired by collecting the part to be processed from different view angles; processing feature recognition is carried out on the part to be processed according to the description statement, the target two-dimensional image and a pre-trained processing feature recognition model, and processing feature information corresponding to the part to be processed is obtained;
the acquiring a target two-dimensional image set corresponding to the part to be processed according to the three-dimensional model of the part to be processed comprises:
acquiring a minimum bounding box of the three-dimensional model, and taking each vertex of the minimum bounding box and each bounding surface central point as a target viewpoint respectively, wherein one bounding surface central point is the central point of one bounding surface of the minimum bounding box;
performing region division on each surrounding surface according to the size of a preset region block to obtain a plurality of surrounding surface sub-regions, obtaining model region complexity corresponding to each surrounding surface sub-region, and obtaining the number of viewpoints corresponding to each surrounding surface sub-region according to the model region complexity and a preset complexity range, wherein the model region complexity corresponding to one surrounding surface sub-region is used for reflecting the fluctuation change degree of the model surface at one side of the three-dimensional model corresponding to the surrounding surface sub-region, and the model region complexity corresponding to one surrounding surface sub-region is obtained according to the variance of the length values of all measurement line segments corresponding to the surrounding surface sub-region;
uniformly adding target viewpoints in each surrounding surface sub-area according to the number of viewpoints corresponding to each surrounding surface sub-area;
and taking the connecting line direction of each target viewpoint and a target central point as the view angle direction of each target viewpoint, acquiring the target two-dimensional images according to each target viewpoint, and marking the view point and the view angle of each target two-dimensional image, wherein the target central point is the central point of the three-dimensional model or the central point of the minimum bounding box.
2. The method for optimizing numerical control machine tool machining process decision according to claim 1, wherein the bounding surface sub-regions are obtained by performing random region division on the bounding surface, and the complexity of a model region corresponding to one bounding surface sub-region is calculated by the following steps:
acquiring measuring points in the surrounding surface sub-area according to the number of preset measuring points, wherein the measuring points are uniformly distributed in the surrounding surface sub-area;
obtaining measurement line segments according to the measurement points, wherein the starting point of the measurement line segment is the measurement point, the end point of the measurement line segment is a point on the surface of the three-dimensional model, and the straight line where each measurement line segment is located is perpendicular to the surrounding surface sub-region;
and calculating the variance of the length values of all the measuring line segments corresponding to the surrounding surface sub-region and taking the variance as the complexity of the model region corresponding to the surrounding surface sub-region.
3. The method of claim 1, wherein the decision variables in the process decision optimization model include a target machine tool selected for each process, a target machining tool selected for each process, a target feed direction selected for each process, a machining order of each process, and cutting parameters of each process.
4. The numerical control machine tool machining process decision optimization method according to claim 3, wherein the preset genetic optimization algorithm is a third generation non-dominated genetic algorithm.
5. The method for optimizing the processing technology decision of the numerical control machine tool according to claim 4, wherein an objective function in the technology decision optimization model is an energy consumption objective function, and the energy consumption objective function is obtained by performing weighted summation according to the machine tool energy consumption of each procedure, the clamping process energy consumption of each procedure, the cutting processing energy consumption of each procedure, the cutter use energy consumption of each procedure and corresponding influence weight values;
and in the optimization solving process, the reciprocal of the objective function is used as a fitness function, and the decision variable is optimized through the third-generation non-dominated genetic algorithm to obtain the objective decision.
6. The numerical control machine tool machining process decision optimization system is characterized by comprising the following steps:
the processing characteristic information acquisition module is used for acquiring processing characteristic information corresponding to a part to be processed, wherein the processing characteristic information comprises a processing characteristic type and a processing area;
the target set acquisition module is used for acquiring a set of target tools, a set of target fixtures and a set of target machine tools according to the processing characteristic information;
the weight setting module is used for setting corresponding influence weight values for machine tool decision, clamp decision, cutting parameter decision and cutter advancing and retreating decision in the machining process respectively;
the decision optimization module is used for establishing a process decision optimization model with the lowest energy consumption as a target according to the processing characteristic information, the set of target cutters, the set of target fixtures, the set of target machine tools and each influence weight value, and performing optimization solution on the process decision optimization model through a preset genetic optimization algorithm to obtain a target decision;
wherein, the processing characteristic information that the part that obtains to wait to process corresponds includes: acquiring a three-dimensional model of the part to be processed and a corresponding description sentence; acquiring a target two-dimensional image set corresponding to the part to be processed according to the three-dimensional model of the part to be processed, wherein the target two-dimensional image set comprises a plurality of target two-dimensional images acquired by collecting the part to be processed from different view angles; processing feature recognition is carried out on the part to be processed according to the description statement, the target two-dimensional image and a pre-trained processing feature recognition model, and processing feature information corresponding to the part to be processed is obtained;
the acquiring a target two-dimensional image set corresponding to the part to be processed according to the three-dimensional model of the part to be processed comprises:
acquiring a minimum bounding box of the three-dimensional model, and taking each vertex of the minimum bounding box and each bounding surface central point as a target viewpoint respectively, wherein one bounding surface central point is the central point of one bounding surface of the minimum bounding box;
performing region division on each surrounding surface according to the size of a preset region block to obtain a plurality of surrounding surface sub-regions, obtaining model region complexity corresponding to each surrounding surface sub-region, and obtaining the number of viewpoints corresponding to each surrounding surface sub-region according to the model region complexity and a preset complexity range, wherein the model region complexity corresponding to one surrounding surface sub-region is used for reflecting the fluctuation change degree of the model surface at one side of the three-dimensional model corresponding to the surrounding surface sub-region, and the model region complexity corresponding to one surrounding surface sub-region is obtained according to the variance of the length values of all measurement line segments corresponding to the surrounding surface sub-region;
uniformly adding target viewpoints in each surrounding surface sub-area according to the number of viewpoints corresponding to each surrounding surface sub-area;
and taking the connecting line direction of each target viewpoint and a target central point as the view angle direction of each target viewpoint, acquiring the target two-dimensional images according to each target viewpoint, and marking the view point and the view angle of each target two-dimensional image, wherein the target central point is the central point of the three-dimensional model or the central point of the minimum bounding box.
7. An intelligent terminal, comprising a memory, a processor and a nc tool machining process decision optimization program stored in the memory and operable on the processor, wherein the nc tool machining process decision optimization program, when executed by the processor, implements the steps of the nc tool machining process decision optimization method according to any one of claims 1 to 5.
8. A computer-readable storage medium, wherein the computer-readable storage medium stores thereon a cnc machining process decision optimizing program, which when executed by a processor implements the steps of the cnc machining process decision optimizing method according to any one of claims 1 to 5.
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